/**
 * Service for managing the prediction model
 * 
 * Note: In a web application, we would typically use TensorFlow.js for model implementation.
 * This is a simplified mock version that simulates the behavior.
 */

import { ModelConfig, PredictionResult } from '../types';

// Mock attention model
class MockTransformerModel {
  private config: ModelConfig;
  private attentionWeights: number[][] = [];
  
  constructor(config: ModelConfig) {
    this.config = config;
    this.initAttentionWeights();
  }
  
  private initAttentionWeights(): void {
    // Initialize with random attention patterns
    const seqLength = 10; // Default sequence length
    this.attentionWeights = Array.from({ length: seqLength }, () => 
      Array.from({ length: seqLength }, () => Math.random())
    );
    
    // Normalize each row
    for (let i = 0; i < seqLength; i++) {
      const sum = this.attentionWeights[i].reduce((a, b) => a + b, 0);
      for (let j = 0; j < seqLength; j++) {
        this.attentionWeights[i][j] /= sum;
      }
    }
  }
  
  // Mock training method
  async train(
    features: number[][],
    diffFeatures: number[][],
    labels: number[],
    onProgress: (progress: number, loss: number, accuracy: number) => void
  ): Promise<void> {
    // Simulate training progress
    const totalEpochs = this.config.epochs;
    let currentLoss = 1.0;
    let currentAccuracy = 0.1;
    
    for (let epoch = 0; epoch < totalEpochs; epoch++) {
      await new Promise<void>((resolve) => {
        setTimeout(() => {
          // Simulate improving metrics
          currentLoss = Math.max(0.1, currentLoss * 0.9);
          currentAccuracy = Math.min(0.95, currentAccuracy * 1.1);
          
          // Report progress
          const progress = ((epoch + 1) / totalEpochs) * 100;
          onProgress(progress, currentLoss, currentAccuracy);
          
          resolve();
        }, 200); // Simulate computation time
      });
    }
    
    // Generate more realistic attention weights based on the sequence
    this.generateRealisticAttentionWeights(features[0].length);
  }
  
  private generateRealisticAttentionWeights(seqLength: number): void {
    this.attentionWeights = Array.from({ length: seqLength }, (_, i) => {
      // Generate weights that pay more attention to recent positions
      return Array.from({ length: seqLength }, (_, j) => {
        // Distance from the end of the sequence - more recent is higher weight
        const recencyFactor = (j + 1) / seqLength;
        // Random factor to add variations
        const randomFactor = 0.5 + Math.random() * 0.5;
        return recencyFactor * randomFactor;
      });
    });
    
    // Normalize each row
    for (let i = 0; i < seqLength; i++) {
      const sum = this.attentionWeights[i].reduce((a, b) => a + b, 0);
      for (let j = 0; j < seqLength; j++) {
        this.attentionWeights[i][j] /= sum;
      }
    }
  }
  
  // Mock prediction method
  predict(sequence: number[], diffSequence: number[]): PredictionResult {
    // Generate probabilities for each possible digit (0-9)
    const probabilities: number[] = Array.from({ length: 10 }, () => Math.random());
    
    // Make last few digits in the sequence influence the prediction more
    const lastDigit = sequence[sequence.length - 1] % 10;
    const secondLastDigit = sequence[sequence.length - 2] % 10;
    
    // Boost probability of the last digit and patterns
    probabilities[lastDigit] += 2;
    probabilities[secondLastDigit] += 1;
    probabilities[(lastDigit + secondLastDigit) % 10] += 3;
    
    // Normalize probabilities
    const sum = probabilities.reduce((a, b) => a + b, 0);
    const normalizedProbs = probabilities.map(p => p / sum);
    
    // Create prediction result
    const predictedValues = normalizedProbs.map((probability, value) => ({
      value,
      probability
    }));
    
    return {
      predictedValues,
      attentionWeights: this.attentionWeights
    };
  }
  
  // Generate a confusion matrix
  generateConfusionMatrix(): number[][] {
    const numClasses = 10; // Digits 0-9
    const matrix = Array.from({ length: numClasses }, () => 
      Array.from({ length: numClasses }, () => 0)
    );
    
    // Diagonal tends to have higher values (correct predictions)
    for (let i = 0; i < numClasses; i++) {
      for (let j = 0; j < numClasses; j++) {
        if (i === j) {
          // Correct predictions (70-90% accuracy)
          matrix[i][j] = 0.7 + Math.random() * 0.2;
        } else {
          // Incorrect predictions (distributed based on digit similarity)
          // Digits like 3 and 8, 1 and 7, 5 and 6 might be confused more often
          const similarity = 
            ((i === 3 && j === 8) || (i === 8 && j === 3)) ||
            ((i === 1 && j === 7) || (i === 7 && j === 1)) ||
            ((i === 5 && j === 6) || (i === 6 && j === 5)) ||
            ((i === 0 && j === 6) || (i === 6 && j === 0)) ||
            ((i === 9 && j === 8) || (i === 8 && j === 9))
              ? 0.05 + Math.random() * 0.1
              : Math.random() * 0.05;
          
          matrix[i][j] = similarity;
        }
      }
    }
    
    // Normalize each row (actual class)
    for (let i = 0; i < numClasses; i++) {
      const sum = matrix[i].reduce((a, b) => a + b, 0);
      for (let j = 0; j < numClasses; j++) {
        matrix[i][j] /= sum;
      }
    }
    
    return matrix;
  }
}

// Mock hyperparameter optimization
export const optimizeHyperparameters = async (
  features: number[][],
  diffFeatures: number[][],
  labels: number[],
  onProgress: (progress: number) => void
): Promise<ModelConfig> => {
  // Simulate hyperparameter optimization with random search
  const numTrials = 10;
  
  for (let trial = 0; trial < numTrials; trial++) {
    await new Promise<void>((resolve) => {
      setTimeout(() => {
        const progress = ((trial + 1) / numTrials) * 100;
        onProgress(progress);
        resolve();
      }, 300);
    });
  }
  
  // Return "optimized" parameters
  return {
    dim: 64,
    depth: 4,
    heads: 4,
    mlpDim: 128,
    dropout: 0.1,
    learningRate: 0.001,
    batchSize: 32,
    epochs: 20
  };
};

export const createModel = (config: ModelConfig): MockTransformerModel => {
  return new MockTransformerModel(config);
};